Best GPUs for Running Local LLMs

Running large language models locally offers privacy, zero API costs, offline access, and the ability to customize models for your specific needs. With the explosion of open-source models like Llama 3, Mistral, Qwen, and others, local inference has never been more accessible.

The key constraint is VRAM. Unlike training, inference primarily needs enough memory to hold the model weights and KV cache. This guide helps you match your hardware to your model ambitions.

7B models (Llama 3 8B, Mistral 7B): 6-8GB VRAM at Q4 quantization, 14-16GB at FP16

13B models: 10-12GB at Q4, 26-28GB at FP16

34B models (CodeLlama 34B): 20-24GB at Q4, 68GB+ at FP16

70B models (Llama 3 70B): 40-48GB at Q4, 140GB+ at FP16

Mixtral 8x7B (MoE): 24-32GB at Q4 due to expert routing

Under $300: Used GTX 1080 Ti (11GB) or RTX 3060 12GB. Good for 7B models quantized.

$300-600: RTX 4060 Ti 16GB or used RTX 3090. The 3090's 24GB opens up 13B+ models.

$600-1000: RTX 4070 Ti Super (16GB) offers excellent performance per watt.

$1000-2000: RTX 4090 (24GB) is the sweet spot. Handles 34B models and fast 70B with quantization.

$2000+: Dual RTX 4090s or used A100 40GB for 70B+ models at higher quality.

Use llama.cpp or Ollama for optimized inference with GGUF quantized models.

Enable Flash Attention 2 for faster inference and lower memory usage.

Consider exl2 quantization for better quality at low bit depths.

For multi-GPU setups, tensor parallelism splits models across cards effectively.

Monitor GPU utilization - if below 90%, you may be CPU or memory bottlenecked.